SoroushVahidi/scidocs huggingface dataset:
Evaluating the Effectiveness of Serious Games for Cultural Awareness: The Icura User Study
(2013)

There is an increasing awareness about the potential of serious games for education and training in many disciplines. However, research still witnesses a lack of methodologies, guidelines and best practices on how to develop effective serious games and how to integrate them in the actual learning and training processes. This process of integration heavily depends on providing and spreading evidence of the effectiveness of serious games This paper reports a user study to evaluate the effectiveness of Icura, a serious game about Japanese culture and etiquette. The evaluation methodology extends the set of instruments used in previous studies by evaluating the effects of the game on raising awareness, by avoiding the selective attention bias and by assessing the medium-term retention. . With this research we aim to provide a handy toolkit for evaluating the effectiveness a serious games for cultural awareness and heritage.
Feature selection via sensitivity analysis of SVM probabilistic outputs
(2007)

Feature selection is an important aspect of solving data-mining and machine-learning problems. This paper proposes a feature-selection method for the Support Vector Machine (SVM) learning. Like most feature-selection methods, the proposed method ranks all features in decreasing order of importance so that more relevant features can be identified. It uses a novel criterion based on the probabilistic outputs of SVM. This criterion, termed Feature-based Sensitivity of Posterior Probabilities (FSPP), evaluates the importance of a specific feature by computing the aggregate value, over the feature space, of the absolute difference of the probabilistic outputs of SVM with and without the feature. The exact form of this criterion is not easily computable and approximation is needed. Four approximations, FSPP1-FSPP4, are proposed for this purpose. The first two approximations evaluate the criterion by randomly permuting the values of the feature among samples of the training data. They differ in their choices of the mapping function from standard SVM output to its probabilistic output: FSPP1 uses a simple threshold function while FSPP2 uses a sigmoid function. The second two directly approximate the criterion but differ in the smoothness assumptions of criterion with respect to the features. The performance of these approximations, used in an overall feature-selection scheme, is then evaluated on various artificial problems and real-world problems, including datasets from the recent Neural Information Processing Systems (NIPS) feature selection competition. FSPP1-3 show good performance consistently with FSPP2 being the best overall by a slight margin. The performance of FSPP2 is competitive with some of the best performing feature-selection methods in the literature on the datasets that we have tested. Its associated computations are modest and hence it is suitable as a feature-selection method for SVM applications.
BzTree: A High-Performance Latch-free Range Index for Non-Volatile Memory
(2018)

Storing a database (rows and indexes) entirely in non-volatile memory (NVM) potentially enables both high performance and fast recovery. To fully exploit parallelism on modern CPUs, modern main-memory databases use latch-free (lock-free) index structures, e.g. Bw-tree or skip lists. To achieve high performance NVMresident indexes also need to be latch-free. This paper describes the design of the BzTree, a latch-free B-tree index designed for NVM. The BzTree uses a persistent multi-word compare-and-swap operation (PMwCAS) as a core building block, enabling an index design that has several important advantages compared with competing index structures such as the Bw-tree. First, the BzTree is latch-free yet simple to implement. Second, the BzTree is fast showing up to 2x higher throughput than the Bw-tree in our experiments. Third, the BzTree does not require any special-purpose recovery code. Recovery is near-instantaneous and only involves rolling back (or forward) any PMwCAS operations that were in-flight during failure. Our end-to-end recovery experiments of BzTree report an average recovery time of 145 μs. Finally, the same BzTree implementation runs seamlessly on both volatile RAM and NVM, which greatly reduces the cost of code maintenance. PVLDB Reference Format: Joy Arulraj, Justin Levandoski, Umar Farooq Minhas,Per-Ake Larson. BzTree: A High-Performance Latch-free Range Index for Non-Volatile Memory. PVLDB, 11(4): xxxx-yyyy, 2018. DOI: https://doi.org/10.1145/3164135.3164147
DynamicFusion: Reconstruction and tracking of non-rigid scenes in real-time
(2015)

We present the first dense SLAM system capable of reconstructing non-rigidly deforming scenes in real-time, by fusing together RGBD scans captured from commodity sensors. Our DynamicFusion approach reconstructs scene geometry whilst simultaneously estimating a dense volumetric 6D motion field that warps the estimated geometry into a live frame. Like KinectFusion, our system produces increasingly denoised, detailed, and complete reconstructions as more measurements are fused, and displays the updated model in real time. Because we do not require a template or other prior scene model, the approach is applicable to a wide range of moving objects and scenes.
Learning Heterogeneous Knowledge Base Embeddings for Explainable Recommendation
(2018)

Providing model-generated explanations in recommender systems is important to user experience. State-of-the-art recommendation algorithms — especially the collaborative filtering (CF)-based approaches with shallow or deep models — usually work with various unstructured information sources for recommendation, such as textual reviews, visual images, and various implicit or explicit feedbacks. Though structured knowledge bases were considered in content-based approaches, they have been largely ignored recently due to the availability of vast amounts of data and the learning power of many complex models. However, structured knowledge bases exhibit unique advantages in personalized recommendation systems. When the explicit knowledge about users and items is considered for recommendation, the system could provide highly customized recommendations based on users’ historical behaviors and the knowledge is helpful for providing informed explanations regarding the recommended items. A great challenge for using knowledge bases for recommendation is how to integrate large-scale structured and unstructured data, while taking advantage of collaborative filtering for highly accurate performance. Recent achievements in knowledge-base embedding (KBE) sheds light on this problem, which makes it possible to learn user and item representations while preserving the structure of their relationship with external knowledge for explanation. In this work, we propose to explain knowledge-base embeddings for explainable recommendation. Specifically, we propose a knowledge-base representation learning framework to embed heterogeneous entities for recommendation, and based on the embedded knowledge base, a soft matching algorithm is proposed to generate personalized explanations for the recommended items. Experimental results on real-world e-commerce datasets verified the superior recommendation performance and the explainability power of our approach compared with state-of-the-art baselines.
Optimal Mass Transport: Signal processing and machine-learning applications
(2017)

Transport-based techniques for signal and data analysis have recently received increased interest. Given their ability to provide accurate generative models for signal intensities and other data distributions, they have been used in a variety of applications, including content-based retrieval, cancer detection, image superresolution, and statistical machine learning, to name a few, and they have been shown to produce state-of-the-art results. Moreover, the geometric characteristics of transport-related metrics have inspired new kinds of algorithms for interpreting the meaning of data distributions. Here, we provide a practical overview of the mathematical underpinnings of mass transport-related methods, including numerical implementation, as well as a review, with demonstrations, of several applications. Software accompanying this article is available from [43].
Design and Experiment of a Conformal Monopulse Antenna for Passive Radar Applications
(2015)

This paper proposed a design scheme of wide band monopulse antenna system for the passive radar seeker (PRS) of anti-radiation missile (ARM). To save the installation space of the PRS, the conical conformal log periodic antennas (LPDA) are employed to form the monopulse antenna system. The specific implementation scheme of PRS based on the conically conformal monopulse antennas was designed and analyzed. A practical monopulse antenna system composed of two log periodic antennas was designed and fabricated over the operating frequency range of 1GHz to 8GHz. A loaded resistor with impedance equal to 50Ω is used to reduce the return loss at low frequency. The experimental results demonstrate that acceptable wide band impedance matching and port isolation for each fabricated antenna was achieved. Furthermore, the wide band sum and difference radiation patterns were formed, which validates the proposed conformal monopulse antenna design scheme for the PRS application.
Prediction of Web Users Browsing Behavior : A Review
(2015)

In Web Usage Mining (WUM), Classification technique is useful in web user prediction, where user’s browsing behavior is predicted. In Web prediction, classification is done first to predict the next Web pages set that a user may visit based on history of previously visited pages. Predicting user’s behavior effectively is important in various applications like recommendation systems, smart phones etc. Currently various prediction systems are available based on Markov model, Association Rule Mining (ARM), classification techniques, etc. In Markov prediction model it cannot predict for a session that was not observed previously in the training set and prediction time is also constant. In ARM, Apriori algorithm requires more time to generate item sets. This paper presents review of various prediction models used to predict web users browsing behavior. Key Words— WUM, classification, Browsing behavior, prediction, ARM, session
Testing and evaluation of a wearable augmented reality system for natural outdoor environments
(2013)

This paper describes performance evaluation of a wearable augmented reality system for natural outdoor environments. Applied Research Associates (ARA), as prime integrator on the DARPA ULTRA-Vis (Urban Leader Tactical, Response, Awareness, and Visualization) program, is developing a soldier-worn system to provide intuitive ‘heads-up’ visualization of tactically-relevant geo-registered icons. Our system combines a novel pose estimation capability, a helmet-mounted see-through display, and a wearable processing unit to accurately overlay geo-registered iconography (e.g., navigation waypoints, sensor points of interest, blue forces, aircraft) on the soldier’s view of reality. We achieve accurate pose estimation through fusion of inertial, magnetic, GPS, terrain data, and computer-vision inputs. We leverage a helmet-mounted camera and custom computer vision algorithms to provide terrain-based measurements of absolute orientation (i.e., orientation of the helmet with respect to the earth). These orientation measurements, which leverage mountainous terrain horizon geometry and mission planning landmarks, enable our system to operate robustly in the presence of external and body-worn magnetic disturbances. Current field testing activities across a variety of mountainous environments indicate that we can achieve high icon geo-registration accuracy (<10mrad) using these vision-based methods.
Position Paper: Physical Unclonable Functions for IoT Security
(2016)

Devices in the Internet of Things (IoT) introduce unique security challenges due to their operating conditions and device limitations. Existing security solutions based on classical cryptography have significant drawbacks in IoT devices, primarily due to the possibility of physical and side channel attacks. As an alternative approach, this position paper advocates the use of physical unclonable functions (PUFs) as a security primitive for developing security solutions for IoT devices. Preliminary work on developing a PUF based mutual authentication protocol is also presented.
Predicting at-risk novice Java programmers through the analysis of online protocols
(2011)

In this study, we attempted to quantify indicators of novice programmer progress in the task of writing programs, and we evaluated the use of these indicators for identifying academically at-risk students. Over the course of nine weeks, students completed five different graded programming exercises in a computer lab. Using an instrumented version of BlueJ, an integrated development environment for Java, we collected novice compilations and explored the errors novices encountered, the locations of these errors, and the frequency with which novices compiled their programs. We identified which frequently encountered errors and which compilation behaviors were characteristic of at-risk students. Based on these findings, we developed linear regression models that allowed prediction of students' scores on a midterm exam. However, the models derived could not accurately predict the at-risk students. Although our goal of identifying at-risk students was not attained, we have gained insights regarding the compilation behavior of our students, which may help us identify students who are in need of intervention.
Cues to deception.
(2003)

Do people behave differently when they are lying compared with when they are telling the truth? The combined results of 1,338 estimates of 158 cues to deception are reported. Results show that in some ways, liars are less forthcoming than truth tellers, and they tell less compelling tales. They also make a more negative impression and are more tense. Their stories include fewer ordinary imperfections and unusual contents. However, many behaviors showed no discernible links, or only weak links, to deceit. Cues to deception were more pronounced when people were motivated to succeed, especially when the motivations were identity relevant rather than monetary or material. Cues to deception were also stronger when lies were about transgressions.
Deception and design: the impact of communication technology on lying behavior
(2004)

Social psychology has demonstrated that lying is an important, and frequent, part of everyday social interactions. As communication technologies become more ubiquitous in our daily interactions, an important question for developers is to determine how the design of these technologies affects lying behavior. The present research reports the results of a diary study, in which participants recorded all of their social interactions and lies for seven days. The data reveal that participants lied most on the telephone and least in email, and that lying rates in face-to-face and instant messaging interactions were approximately equal. This pattern of results suggests that the design features of communication technologies (e.g., synchronicity, recordability, and copresence) affect lying behavior in important ways, and that these features must be considered by both designers and users when issues of deception and trust arise. The implications for designing applications that increase, decrease or detect deception are discussed.
Psychological aspects of natural language. use: our words, our selves.
(2003)

The words people use in their daily lives can reveal important aspects of their social and psychological worlds. With advances in computer technology, text analysis allows researchers to reliably and quickly assess features of what people say as well as subtleties in their linguistic styles. Following a brief review of several text analysis programs, we summarize some of the evidence that links natural word use to personality, social and situational fluctuations, and psychological interventions. Of particular interest are findings that point to the psychological value of studying particles-parts of speech that include pronouns, articles, prepositions, conjunctives, and auxiliary verbs. Particles, which serve as the glue that holds nouns and regular verbs together, can serve as markers of emotional state, social identity, and cognitive styles.
Testing the Interactivity Model: Communication Processes, Partner Assessments, and the Quality of Collaborative Work
(1999)

EE K. BuRGOON IS Professor of Communication and Director of the Center for the Management of Information at the University of Arizona. She is the author or coauthor of seven books and nearly two hundred articles and chapters on topics related to interpersonal and nonverbal communication, deception and credibility, mass media, and new communication technologies. Her current research focuses on interactivity, adaptation, and attunement in communication.
Male and Female Spoken Language Differences : Stereotypes and Evidence
(2005)

Male speech and female speech have been observed to differ in their form, topic, content, and use. Early writers were largely introspective in their analyses; more recent work has begun to provide empirical evidence. Men may be more loquacious and directive; they use more nonstandard forms, talk more about sports, money, and business, and more frequently refer to time, space, quantity, destructive action, perceptual attributes, physical movements, and objects. Women are often more supportive, polite, and expressive, talk more about home and family, and use more words implying feeling, evaluation, interpretation, and psychological state. A comprehensive theory of "genderlect" must include information about linguistic features under a multiplicity of conditions.
Study of Deep Learning Techniques for Side-Channel Analysis and Introduction to ASCAD Database
(2018)

To provide insurance on the resistance of a system against side-channel analysis, several national or private schemes are today promoting an evaluation strategy, common in classical cryptography, which is focussing on the most powerful adversary who may train to learn about the dependency between the device behaviour and the sensitive data values. Several works have shown that this kind of analysis, known as Template Attacks in the side-channel domain, can be rephrased as a classical Machine Learning classification problem with learning phase. Following the current trend in the latter area, recent works have demonstrated that deep learning algorithms were very efficient to conduct security evaluations of embedded systems and had many advantages compared to the other methods. Unfortunately, their hyper-parametrization has often been kept secret by the authors who only discussed on the main design principles and on the attack efficiencies. This is clearly an important limitation of previous works since (1) the latter parametrization is known to be a challenging question in Machine Learning and (2) it does not allow for the reproducibility of the presented results. This paper aims to address these limitations in several ways. First, completing recent works, we propose a comprehensive study of deep learning algorithms when applied in the context of side-channel analysis and we discuss the links with the classical template attacks. Secondly, we address the question of the choice of the hyper-parameters for the class of multi-layer perceptron networks and convolutional neural networks. Several benchmarks and rationales are given in the context of the analysis of a masked implementation of the AES algorithm. To enable perfect reproducibility of our tests, this work also introduces an open platform including all the sources of the target implementation together with the campaign of electromagnetic measurements exploited in our benchmarks. This open database, named ASCAD, has been specified to serve as a common basis for further works on this subject. Our work confirms the conclusions made by Cagli et al. at CHES 2017 about the high potential of convolutional neural networks. Interestingly, it shows that the approach followed to design the algorithm VGG-16 used for image recognition seems also to be sound when it comes to fix an architecture for side-channel analysis.
Mnemonic Descent Method: A Recurrent Process Applied for End-to-End Face Alignment
(2016)

Cascaded regression has recently become the method of choice for solving non-linear least squares problems such as deformable image alignment. Given a sizeable training set, cascaded regression learns a set of generic rules that are sequentially applied to minimise the least squares problem. Despite the success of cascaded regression for problems such as face alignment and head pose estimation, there are several shortcomings arising in the strategies proposed thus far. Specifically, (a) the regressors are learnt independently, (b) the descent directions may cancel one another out and (c) handcrafted features (e.g., HoGs, SIFT etc.) are mainly used to drive the cascade, which may be sub-optimal for the task at hand. In this paper, we propose a combined and jointly trained convolutional recurrent neural network architecture that allows the training of an end-to-end to system that attempts to alleviate the aforementioned drawbacks. The recurrent module facilitates the joint optimisation of the regressors by assuming the cascades form a nonlinear dynamical system, in effect fully utilising the information between all cascade levels by introducing a memory unit that shares information across all levels. The convolutional module allows the network to extract features that are specialised for the task at hand and are experimentally shown to outperform hand-crafted features. We show that the application of the proposed architecture for the problem of face alignment results in a strong improvement over the current state-of-the-art.
BigDataScript: a scripting language for data pipelines
(2015)

MOTIVATION
The analysis of large biological datasets often requires complex processing pipelines that run for a long time on large computational infrastructures. We designed and implemented a simple script-like programming language with a clean and minimalist syntax to develop and manage pipeline execution and provide robustness to various types of software and hardware failures as well as portability.


RESULTS
We introduce the BigDataScript (BDS) programming language for data processing pipelines, which improves abstraction from hardware resources and assists with robustness. Hardware abstraction allows BDS pipelines to run without modification on a wide range of computer architectures, from a small laptop to multi-core servers, server farms, clusters and clouds. BDS achieves robustness by incorporating the concepts of absolute serialization and lazy processing, thus allowing pipelines to recover from errors. By abstracting pipeline concepts at programming language level, BDS simplifies implementation, execution and management of complex bioinformatics pipelines, resulting in reduced development and debugging cycles as well as cleaner code.


AVAILABILITY AND IMPLEMENTATION
BigDataScript is available under open-source license at http://pcingola.github.io/BigDataScript.